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Intelligence coupled with image processing techniques helps in proper water management thereby increasing yield.
1.5.5 Crop Harvesting
It is reported that about 40% of the annual agricultural cost is being spent on labor employment. Nowadays, AI-based robots are being deployed to reduce labor costs. Artificial Intelligence also finds application in supply chain management [22] for crops.
1.6 Indian Agriculture and Smart Farming
As the population increases, the traditional methods of agriculture are not suitable, and adopting the latest technologies is essential for improving productivity and farmers’ income. The revolution in the latest technologies like Big Data, Cloud Computing, Internet of Things, and Satellite, and Drone-based image analysis has made a big impact in many Indian industries from IT to health care. Nearly 450 start-ups from India are using the latest technologies like AI, ML, blockchain, big data, drones, and IoT with the main aim of increasing agricultural productivity [24]. Gobasco is a private concern in Lucknow which offers an online marketplace for best buying and selling supply goods. Gramophone in Indore provides chatbots for farmers, which help farmers to have better crop cycles. Jivabhumi, Bengaluru uses blockchain technology to bridge the gap between farmers and consumers. Digital farming is adopted in the states of Gujarat, Maharashtra, and Rajasthan by Agrostar, Pune, which helps farmers 24/7 to know the crop requirements along with required services [26].
According to the NASSCOM report 2019, smart farming is predicted to reach 23.14 billion dollars by 2022. The following are some of the smart farming applications:
precision farming uses IoT sensors in the field, and farmers can view the data through mobile applications;
agricultural drones manage large farms using airborne sensors, which are capable of collecting thermal and visual data;
livestock monitoring helps to detect sick animals;
smart greenhouses use solar-powered sensors, which help the farmer remotely turn on/off the lights, fan, monitor temperature, and humidity, and so on;
smart irrigation uses a sprinkler system along with an alert system;
farm management system for collecting and storing sensor data in the cloud and access through various devices and applications;
apart from soil health monitoring, optimal seed sowing, water management, and weed management, AI also helps in identifying plant diseases.
1.6.1 Sensors for Smart Farming
Sensors collect data from crops, livestock, soil, and the atmosphere. The sensor data are supplied to a cloud-based IoT platform with preprogrammed decision rules and models that determine the status of the studied object and highlight any flaw or requirement. Following the discovery of difficulties, the IoT platform’s user and/or ML-driven components assess whether and which location-specific treatments are required. Various sensors used for smart farming are shown in Figure 1.8.
Electromagnetic sensors detect the ability of soil particles to conduct or acquire electrical charge using electric circuits. The electrical conductivity of the soil is immediately recorded in a logger after it becomes a member of the electromagnetic circuit. To measure electrical conductivity, contact EC sensors use electrodes and make contact with the soil. The data, as well as the position information, are stored in a data logger. Contact EC sensors are highly common in precision agriculture because of their large area coverage and less prone to outside interference.
Figure 1.8 Various sensors for smart farming.
Optoelectronic devices allow for such interaction. In general, optoelectronic sensors use the properties of various materials to create a spectral signature. A common type of sensor in this category is the soil organic matter (SOM) sensor that uses the optical method.
An ion-selective electrode is used for some measurements. The activity of certain ions is detected by these electrodes. The mechanical resistance of the soil is measured by mechanical sensors. Airflow sensors are used to determine the air permeability of the soil. The sensor detects the pressure required to compress a specific volume of air into the soil at a specific depth.
Acoustic sensors: The change in noise level caused by the tool’s interaction with the soil particles is used to evaluate soil texture. Soil pH sensors detect acidity and alkalinity in the soil, which can lead to fertility issues, stunted growth, oddly colored leaves, and poor plant health. Ammonium sensors detect exact ammonia and nitrogen concentration and nitrate sensors are devices that detect the presence of nitrate in the soil or water.
Potassium sensors: These devices monitor potassium levels in the soil to better understand the relationship between free potassium in soil nutrients reserves, soil texture, and root growth, which could lead to more efficient fertilizer use. Soil-water sensors are useful for detecting local energy and water balances, delivery of applied chemicals to plants and groundwater, irrigation management, and precision farming.
1.7 Advantages of Using AI in Agriculture
In agriculture, AI aids farmers in comprehending data insights, such as temperature, precipitation, wind speed, and solar power.
1 i. Essential plants can be produced, harvested, and marketed more efficiently with AI.
2 ii. The focus of AI implementation is on detecting defective crops and improving the chances of healthy crop production.
3 iii. Agro-based businesses have benefited from the advancement of AI technology.
4 iv. AI is being used in applications such as weather forecasting and automated machine adjustments.
Artificial Intelligence has the potential to improve crop management practices, allowing many tech companies to use advanced algorithms
1.8 Role of AI in Indian Agriculture
In India, adoption of modern technologies referred to as Agritech developments in various verticals like smart irrigation, weather technology solutions, and so on. The Government of India has initiated a new program called AGRI-UDAAN with the aim to boost innovations and entrepreneurship in India. Indian agriculture also attracts a foreign direct investment equity inflow of about 2.45 billion dollars (according to DIPP). To meet one-sixth of the total Indian economy, we need nearly half of India’s land and huge labor.
Various AI and ML methods are employed in predictive agricultural analytics to anticipate the best time to sow seeds, receive alerts on upcoming pest attacks, and so on. Artificial Intelligence in agriculture enables the most efficient use of farming data, allowing types of equipment such as smart drones, autonomous tractors, soil sensors, and Agri-bots to support smart farming.
In the fiscal year 2019 to 2020, 133 agreements raised more than $1 billion for Indian agrifood tech start-ups. India’s agricultural exports increased to $37.4 billion in 2019, and this is expected to rise further with improvements in the supply chain, as well as better storage and packaging. All of these measures will go a long way toward ensuring farmers receive fair pricing and reducing agrarian stress. Investments in technology are helping to boost agricultural output and productivity even further. Disruptive technologies, such as AI, are transforming Indian agriculture, and an increasing number of agri-tech businesses are developing and implementing AI-based solutions.
Artificial Intelligence has the potential to improve farm production, alleviate